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Confusion Matrix

Build a confusion matrix for binary classification results.

Given lists of true labels and predicted labels (each 0 or 1), compute the 2x2 confusion matrix:

$\begin{bmatrix} TN & FP \\ FN & TP \end{bmatrix}$

where:

Return the matrix as a 2D list [[TN, FP], [FN, TP]].

Input:

y_true = [1, 0, 1, 1, 0, 0]
y_pred = [1, 0, 0, 1, 0, 1]

Output:

[[2, 1], [1, 2]]

Reasoning:

We iterate over the y_true and y_pred lists simultaneously, comparing each pair of true and predicted labels.
For each pair, we check the conditions for TP, TN, FP, and FN and increment the corresponding counter:
- TP if $y_{true} = 1$ and $y_{pred} = 1$ ,
- TN if $y_{true} = 0$ and $y_{pred} = 0$ ,
- FP if $y_{true} = 0$ and $y_{pred} = 1$ ,
- FN if $y_{true} = 1$ and $y_{pred} = 0$ .
After iterating over all pairs, we count:
- TN: 2 (for the pairs (0,0) at indices 1 and 4),
- FP: 1 (for the pair (0,1) at index 5),
- FN: 1 (for the pair (1,0) at index 2),
- TP: 2 (for the pairs (1,1) at indices 0 and 3).
The final output is the 2x2 confusion matrix: $[[TN, FP], [FN, TP]] = [[2, 1], [1, 2]]$ .

Editor

Python 3.13.1

0/0

Run code to see test results.